The present inventive subject matter relates generally to the art of automated cameras. Particular but not exclusive relevance is found in connection with red light and/or other traffic cameras. Accordingly, the present specification makes specific reference thereto. It is to be appreciated however that aspects of the present inventive subject matter are also equally amenable to other like applications.
To capture high quality images with red light, traffic and/or other like automated and/or unattended cameras it is commonly desirable to have an unobstructed field of view (FoV) in which objects of interest may be located. Should the FoV be obstructed, objects of interest, e.g., such as vehicles, drivers and/or license plates, may not be accurately visualized and/or identifiable in images captured by the camera. For example, accurate visualization and/or identification of such objects in captured images are often important for law enforcement purposes and/or the issuing of traffic citation.
Over time, a camera's FoV may become obstructed by an object in the FoV near the camera. For example, while not initially present, obstructions near the camera may appear due to the growth of plant foliage, ice build-up on the camera lens or porthole, graffiti or debris on the camera lens or porthole, etc. Such obstructions can sufficiently block or obscure various regions sought to be captured in an image obtained by the camera. In turn, one or more objects of interest otherwise sought to be captured in such an image may not be sufficiently visualized and/or readily identifiable in the image. Accordingly, law enforcement or other actions reliant on accurate visualization and/or identification of one or more target objects in a captured image may be frustrated. Moreover, some more advance camera systems may be triggered to capture an image in response to events occurring in a scene observed by the camera, e.g., such as the detection of a vehicle or vehicle movement within the scene. Where such an event is obscured from view by an obstruction, the camera may not capture an otherwise desired image because the event was not detected.
Traditionally, operators of automated/unattended cameras such as those mentioned above relied on human labor-intensive practices to monitor, check and/or verify obstruction-free operation. For example, an operator may periodically or intermittently conduct a manual review of images obtained from a camera and visually inspect them for obstructions. Such an operator may commonly be assigned a significant number of cameras to check on a fairly frequent basis. Accordingly, such a process can be repetitive and prone to human oversight and/or error. Additionally, a maintenance technician may be assigned to manually inspect camera installations in the field at periodic or intermittent intervals. Again, this is a labor-intensive process prone to human oversight and/or error.
Alternately, automated methods have been developed to detect camera obstructions from an obtained test image. For example, one such method employs a reference image obtained from an unobstructed camera. In this case, the reference image and test image are subtracted from one another to detect variations therebetween, wherein a detected variation is then deemed indicative of an obstruction in the test image. Such subtractive methods, however, can have certain limitations and/or drawbacks. For example, in dynamically changing scenes, e.g., such as a traffic intersection, objects and/or object locations within the scene may vary from image to image. For example, different vehicles or pedestrians may appear in different images or appear at different locations within different images. Accordingly, by image subtraction from a reference which may not include the same dynamically changing elements, the resulting variations may falsely indicate an obstruction. Additionally, a change in the camera alignment and/or imaging conditions (e.g., such as illumination level) may produce variations in the subtracted image which can again lead to a false indication of an obstruction. Accordingly, such subtraction methods commonly have to account for dynamically varying scenes in order to accurately detect obstructions. The image subtraction and/or aforementioned accounting for dynamically varying scenes can be time intensive and may put further demands and/or complexity on a processor executing the same. Therefore, it has heretofore remained desirable to have an obstruction detection method that is not dependent upon a reference image in this manner.
Accordingly, a new and/or improved method, system and/or apparatus for monitoring, detecting and/or reporting obstructions in a camera's FoV is disclosed which addresses the above-referenced problem(s) and/or others.